Revised edition of the authors' Ethics in psychology and the mental health professions, 2008.

"Written in a highly readable and accessible style, this new edition retains the key features that have contributed to its popularity, including hundreds of case studies that provide illustrative guidance on a wide variety of topics, including fee setting, advertising for clients, research ethics, sexual attraction, how to confront observed unethical conduct in others, and confidentiality. Ethics in Psychology and the Mental Health Professions will be important reading for practitioners and students in training."--BOOK JACKET.

Tiivistelmä. - Resumo en Esperanto. - Rezjume.

Since the 1930s, organizing movements for social justice in the U.S. have largely been built on secular assumptions. But what if Christians were to shape their organizing around the implications of the truth that God is real and Jesus is risen? Reverend Alexia Salvatierra and theologian Peter Heltzel propose a model of organizing that arises from their Christian convictions, with implications for all faiths.

This book is about the social psychological dynamics and phenomenology of social inclusion and exclusion. The editors take as their starting point the assumption that social life is conducted in a framework of relationships in which individuals seek inclusion and belongingness. Relationships necessarily include others, but equally they have boundaries that exclude. Frequently these boundaries are challenged or crossed. The book will draw together research on individual motivation, small group processes, stigmatization and intergroup relations, to provide a comprehensive social psychological account of social inclusion and exclusion.

A guide for using computational text analysis to learn about the social world From social media posts and text messages to digital government documents and archives, researchers are bombarded with a deluge of text reflecting the social world. This textual data gives unprecedented insights into fundamental questions in the social sciences, humanities, and industry. Meanwhile new machine learning tools are rapidly transforming the way science and business are conducted. Text as Data shows how to combine new sources of data, machine learning tools, and social science research design to develop and evaluate new insights. Text as Data is organized around the core tasks in research projects using text—representation, discovery, measurement, prediction, and causal inference. The authors offer a sequential, iterative, and inductive approach to research design. Each research task is presented complete with real-world applications, example methods, and a distinct style of task-focused research. Bridging many divides—computer science and social science, the qualitative and the quantitative, and industry and academia—Text as Data is an ideal resource for anyone wanting to analyze large collections of text in an era when data is abundant and computation is cheap, but the enduring challenges of social science remain. Overview of how to use text as data Research design for a world of data deluge Examples from across the social sciences and industry